Neural ODE-based Imitation Learning (NODE-IL): Data-Efficient Imitation Learning for Long-Horizon Multi-Skill Robot Manipulation

• Main Authors: Zhao, Shiyao, Xu, Yucheng, Kasaei, Mohammadreza, Khadem, Mohsen, Li, Zhibin
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Data models; Imitation learning; Manipulator dynamics; Mathematical models; Robust control; Robustness; Scalability; Training; Translation
• ISSN: 2153-0866
• DOI: 10.1109/IROS58592.2024.10802736

In robotics, acquiring new skills through Imitation Learning (IL) is crucial for handling diverse complex tasks. However, model-free IL faces challenges of data inefficiency and prolonged training time, whereas model-based methods struggle to obtain accurate nonlinear models. To address these challenges, we developed Neural ODE-based Imitation Learning (NODE-IL), a novel model-based imitation learning framework that employs Neural Ordinary Differential Equations (Neural ODEs) for learning task dynamics and control policies. NODE-IL comprises (1) Dynamic-NODE for learning the continuous differentiable task's transition dynamics model, and (2) Control-NODE for learning a long-horizon control policy in an MPC fashion, which are trained holistically. Extensively evaluated on challenging manipulation tasks, NODE-IL demonstrates significant advantages in data efficiency, requiring less than 70 samples to achieve robust performance. It outperforms Behavioral Cloning from Observation (BCO) and Gaussian Process Imitation Learning (GP-IL) methods, achieving 70% higher average success rate, and reducing translation errors for high-precision tasks, which demonstrates its robustness and accuracy, as an effective and efficient imitation learning approach for learning complex manipulation tasks.